|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/the-econometrics-of-time-series-data
课程评论：没有评论
课程名称：时间序列数据的计量经济学 课程概述：本课程探讨用于处理时间序列数据挑战的模型和方法。讨论重点在于特定模型的使用动机，以及时间序列数据的特征，特别是可能的记忆效应。您将： - 讨论时间序列模型，这些模型涉及在同一时间段内对一个或多个变量进行观察的数据。 - 探索平稳和非平稳时间序列模型，以及非平稳数据与趋势平稳过程之间的区别。 - 考虑非平稳数据可能引起的问题。 - 发现时间序列模型在建模经济体GDP增长和检验购买力平价假说中的应用。 - 探索使用计量经济学模型进行预测的概念。 - 讨论决定模型在样本内和样本外预测表现的不同标准。 - 探索方差不恒定的数据问题，以及波动率预测的模型。 - 使用真实金融市场数据估计ARCH(p)和GARCH(p,q)模型，并展示如何通过均值中的Garch扩展这些模型。 建议您在学习本课程之前完成并理解专业方向中的前三个课程：《经典线性回归模型》、《计量经济学中的假设检验》和《应用计量经济学专题》。 课程结束时，您将能够： - 操作和绘制不同类型的数据。 - 估计并解释经验自相关函数。 - 估计并比较平稳系列的模型。 - 测试时间序列数据的非平稳性。 - 估计和解释协整方程。 - 执行样本内外的预测练习。 - 估计并比较变化波动率的模型。 课程大纲： 第1部分：时间序列数据 本周的材料展示了一系列时间序列观测。我们讨论白噪声、趋势平稳和非平稳时间序列，同时研究GDP和金融市场的实际观察。 第2部分：平稳时间序列模型 本周我们处理平稳时间序列模型，介绍白噪声、移动平均、自回归及自回归移动平均模型，并探讨估计自相关和移动平均模型阶数的问题。 第3部分：非平稳时间序列模型 本周考察非平稳时间序列观察所带来的问题，定义时间序列数据的非平稳性，并介绍测试和协整的相关模型。 第4部分：变化波动率的模型 本周讨论金融市场收益中的一些特征，如波动聚集和聚合正态性，介绍非线性模型的估计问题，并探讨ARCH和GARCH等模型的优缺点。
Part: 1
Title:Time Series Data
Description:This week’s materials present a number of time series observations. We look at white noise, trend stationary and non-stationary time series. We explore both at real observation about the GDP and to financial markets observations, and to generated series of data. We introduce both the idea of autocorrelation function and that of partial autocorrelation function as tools to understand the degree of persistency in a series of data.
Part: 2
Title:Stationary Time Series Models
Description:This week we deal with stationary time series models. We present white noise, moving average, autoregression and autoregressive and moving average models. We describe the models and the different types of autocorrelation functions you have in each of these cases. We also discuss the problem of estimating the order of the autocorrelation and moving average models. We study the idea and the challenges raised by forecasting, and that’s raised by high persistency of the impact of shocks on the observed series.
Part: 3
Title:Non-Stationary Time Series Models
Description:This week we consider the problems raised by non-stationarity of time series observations. We define non-stationarity of time series data, and present the tests for non-stationarity, including the challenges raised by near non-stationarity, and that of potential correlation of the estimating model when testing for non-stationarity. We present a full example to show what are the consequences in cases where we adopt the classical linear regression model when observations are non-stationary. We introduce the idea of cointegration and present introductory models to test whether the variables are cointegrated.
Part: 4
Title:Models for Changing Volatility
Description:This week’s materials discuss some stylised facts present across financial market returns, independent of the period, the financial tool and the market we study, that are volatility clustering and aggregational gaussianity. We discuss why these models, being nonlinear in nature, cannot be estimated via the classical linear regression model, and discuss and estimate some examples of autoregressive conditional heteroscedastic models. We discuss advantages and shortcomings of these models; building on the latter, we present some generalisation of the approach to generalised conditional heteroscedastic models (GARCH), GARCH-in-meena, TGARCH amd IGRACH models.
In this course, you will look at models and approaches that are designed to deal with challenges raised by time series data. The discussion covers the motivation for the use of particular models and the description of the characteristics of time series data, with a special attention raised to the potential memory. You will: – Discuss time series models, that refer to data that have been collected over a period on one or more variables for the same individual. – Explore both on stationary and non-stationary time series models, as well as the difference between the non-stationary data and the trend-stationary processes – Consider the problems that may occur with non-stationarity data. – Discover the applications of time series models that are of use when we want to model the GDP growth of an economy, and to test for the Purchasing Power Parity Hypothesis. – Explore the idea of forecasting using econometric models. – Discuss different criteria to decide how good your in-sample and out-of-sample forecasts are. – Explore the problem raised by data where the variance is non-constant, and models for volatility forecasting. – Estimate ARCH(p) and GARCH(p,q) models for volatility with real financial market data and present how to extend these models to the mean of the time series via Garch-in-mean. It is recommended that you have completed and understood the previous three courses in this Specialisation: The Classical Linear Regression Model, Hypothesis Testing in Econometrics and Topics in Applied Econometrics. By the end of this course, you will be able to: – Manipulate and plot the different types of data – Estimate and interpret the empirical autocorrelation function – Estimate and compare models for stationary series – Test for non-stationarity of time series data – Estimate and interpret cointegration equations – Perform in-sample and out-of-sample forecasting exercises – Estimate and compare models for changing volatility
